We demonstrate the effectiveness of our approach on the COCO2017 benchmark [1]} using diverse detectors, including the relatively large two-stage Faster RCNN and single-stage RetinaNet used in previous knowledge distillation works, as well as more compact detectors, such as SSD300, SSD512 [2]} and Faster RCNNs[3]} with lightweight backbones. Our classifier-to-detector distillation approach outperforms the detector-to-detector distillation ones in the presence of compact students, and helps to further boost the performance of detector-to-detector distillation techniques for larger ones, such as Faster RCNN and RetinaNet with a ResNet50 backbone. We will make our code publicly available.
